Lake or Cesspool? The Challenges of Big Data Infrastructure
Big data environments are making it quick and easy for companies to store any and all forms of audience and customer. Marketers are increasingly seeking to activate this data to power targeted marketing and personalization.
The concept of a “data lake” has emerged as a form of big data repository to support a traditional data warehouse. A data lake stores large volumes of data in structured, semistructured, or unstructured form without the need to define a data schema up-front. This not only allows for an easy and nimble way to capture data, but also provides agile and granular access for analytics.
A well-conceived data lake should empower data scientists and marketing analysts to mine insights and identify new attributes for targeted marketing or predictive modeling. This enables organizations to focus on extracting and processing only those data elements that will drive the highest business value. As a result the right data gets incorporated into the more structured data aggregates and attributes, which, in turn, power marketing execution. However, the ability to collect data from a multitude of sources at an expedient rate can create an opening for data lakes, or storage systems, to become a quagmire of information that is not easy to understand or to act upon.
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How good is your big data in terms of quality?
Do you have the necessary taxonomy and cross-reference data to analyze and aggregate data from the data lake?
How easy is it to integrate your big data with other important (traditional) data environments?
How does big data fit into your overall data strategy?
Do we have a governance process in place to maintain the integrity of the data lake over time?